pogreb

Pogreb

Pogreb is an embedded key-value store for read-heavy workloads written in Go.

Key characteristics

• 100% Go.
• Optimized for fast random lookups and infrequent bulk inserts.
• Can store larger-than-memory data sets.
• Low memory usage.
• All DB methods are safe for concurrent use by multiple goroutines.

Installation

$ go get -u github.com/akrylysov/pogreb

Usage

Opening a database

To open or create a new database, use the pogreb.Open() function:

package main

import (
	"log"

	"github.com/akrylysov/pogreb"
)

func main() {
    db, err := pogreb.Open("pogreb.test", nil)
    if err != nil {
        log.Fatal(err)
        return
    }	
    defer db.Close()
}

Writing to a database

Use the DB.Put() function to insert a new key-value pair:

err := db.Put([]byte("testKey"), []byte("testValue"))
if err != nil {
	log.Fatal(err)
}

Reading from a database

To retrieve the inserted value, use the DB.Get() function:

val, err := db.Get([]byte("testKey"))
if err != nil {
	log.Fatal(err)
}
log.Printf("%s", val)

Deleting from a database

Use the DB.Delete() function to delete a key-value pair:

err := db.Delete([]byte("testKey"))
if err != nil {
	log.Fatal(err)
}

Iterating over items

To iterate over items, use ItemIterator returned by DB.Items():

it := db.Items()
for {
    key, val, err := it.Next()
    if err == pogreb.ErrIterationDone {
    	break
    }
    if err != nil { 
        log.Fatal(err)
    }
    log.Printf("%s %s", key, val)
}

Performance

The benchmarking code can be found in the pogreb-bench repository.

Results of read performance benchmark of pogreb, goleveldb, bolt and badgerdb on DigitalOcean 8 CPUs / 16 GB RAM / 160 GB SSD + Ubuntu 16.04.3 (higher is better):

Internals

Design document.

Limitations

The design choices made to optimize for point lookups bring limitations for other potential use-cases. For example, using a hash table for indexing makes range scans impossible. Additionally, having a single hash table shared across all WAL segments makes the recovery process require rebuilding the entire index, which may be impractical for large databases.